 Price-aware enhanced dynamic recommendation based on deep learningWenhao Guo, Jin Tian and Minqiang Li Journal of Retailing and Consumer Services, 2023, vol. 75, issue C Abstract: Price is one of the essential elements influencing consumer purchase behavior. Like consumers’ preferences in products, their price preferences also dynamically change over time. However, dynamic price preferences haven’t been fully considered in existed recommendation studies. In this study, we propose a deep learning-based dynamic recommendation model by considering consumers’ dynamic preferences in both product and price. We specially design a review-and-rating-based sequence generator to select products whose prices the consumers are satisfied with to form the new purchase sequence. We also develop a multi-level attention mechanism in the transformer layer to explore the correlations between consumers’ price choices and to combine the price preferences with the product preferences. Experimental results show the proposed model outperforms the state-of-the-art models on some real-world datasets. Our findings can help retailers understand consumers’ price preferences and make informed decisions related to pricing, discounting, and bundle sales strategies. Keywords: Price preference; Dynamic preference; Consumer review; Deep learning; Personalized recommendation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:75:y:2023:i:c:s0969698923002473 DOI: 10.1016/j.jretconser.2023.103500 Access Statistics for this article Journal of Retailing and Consumer Services is currently edited by Harry Timmermans More articles in Journal of Retailing and Consumer Services from Elsevier |
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